Temporal Generative

Temporal generative models aim to create realistic and consistent sequences of data over time, addressing challenges in predicting future states or generating novel temporal patterns. Current research focuses on integrating spatial information, leading to the development of spatio-temporal models using architectures like transformers, diffusion models, and recurrent neural networks, often tailored for specific data types (e.g., video, traffic flow). These advancements have significant implications for various fields, improving accuracy in applications such as traffic prediction, autonomous driving, and medical image analysis by enabling more accurate modeling of dynamic systems.